# -*- encoding: utf-8 -*-
"""
@File    : resnet.py
@Time    : 2019/10/14 20:54
@Author  : Keen
@Software: PyCharm
"""

import torch
import torch.nn as nn
import torch.nn.functional as F


# 用于ResNet18和34的残差块，用的是2个3x3的卷积
class BasicBlock(nn.Module):
	expansion = 1

	def __init__(self, in_planes, planes, stride=1):
		super(BasicBlock, self).__init__()
		self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,
		                       stride=stride, padding=1, bias=False)
		self.bn1 = nn.BatchNorm2d(planes)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
		                       stride=1, padding=1, bias=False)
		self.bn2 = nn.BatchNorm2d(planes)
		self.shortcut = nn.Sequential()
		# 经过处理后的x要与x的维度相同(尺寸和深度)
		# 如果不相同，需要添加卷积+BN来变换为同一维度
		if stride != 1 or in_planes != self.expansion * planes:
			self.shortcut = nn.Sequential(
				nn.Conv2d(in_planes, self.expansion * planes,
				          kernel_size=1, stride=stride, bias=False),
				nn.BatchNorm2d(self.expansion * planes)
			)

	def forward(self, x):
		out = F.relu(self.bn1(self.conv1(x)))
		out = self.bn2(self.conv2(out))
		out += self.shortcut(x)
		out = F.relu(out)
		return out


# 用于ResNet50,101和152的残差块，用的是1x1+3x3+1x1的卷积
class Bottleneck(nn.Module):
	# 前面1x1和3x3卷积的filter个数相等，最后1x1卷积是其expansion倍
	expansion = 4

	def __init__(self, in_planes, planes, stride=1):
		super(Bottleneck, self).__init__()
		self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
		self.bn1 = nn.BatchNorm2d(planes)
		self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
		                       stride=stride, padding=1, bias=False)
		self.bn2 = nn.BatchNorm2d(planes)
		self.conv3 = nn.Conv2d(planes, self.expansion * planes,
		                       kernel_size=1, bias=False)
		self.bn3 = nn.BatchNorm2d(self.expansion * planes)

		self.shortcut = nn.Sequential()
		if stride != 1 or in_planes != self.expansion * planes:
			self.shortcut = nn.Sequential(
				nn.Conv2d(in_planes, self.expansion * planes,
				          kernel_size=1, stride=stride, bias=False),
				nn.BatchNorm2d(self.expansion * planes)
			)

	def forward(self, x):
		out = F.relu(self.bn1(self.conv1(x)))
		out = F.relu(self.bn2(self.conv2(out)))
		out = self.bn3(self.conv3(out))
		out += self.shortcut(x)
		out = F.relu(out)
		return out


class ResNet(nn.Module):
	def __init__(self, block, num_blocks, num_classes=10):
		super(ResNet, self).__init__()
		self.in_planes = 64

		self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
		                       stride=1, padding=1, bias=False)
		self.bn1 = nn.BatchNorm2d(64)

		self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
		self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
		self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
		self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
		self.linear = nn.Linear(512 * block.expansion, num_classes)

	def _make_layer(self, block, planes, num_blocks, stride):
		strides = [stride] + [1] * (num_blocks - 1)
		layers = []
		for stride in strides:
			layers.append(block(self.in_planes, planes, stride))
			self.in_planes = planes * block.expansion
		return nn.Sequential(*layers)

	def forward(self, x):
		out = F.relu(self.bn1(self.conv1(x)))
		out = self.layer1(out)
		out = self.layer2(out)
		out = self.layer3(out)
		out = self.layer4(out)
		out = F.avg_pool2d(out, 4)
		out = out.view(out.size(0), -1)
		out = self.linear(out)
		return out


def ResNet18():
	return ResNet(BasicBlock, [2, 2, 2, 2])


def ResNet34():
	return ResNet(BasicBlock, [3, 4, 6, 3])


def ResNet50():
	return ResNet(Bottleneck, [3, 4, 6, 3])


def ResNet101():
	return ResNet(Bottleneck, [3, 4, 23, 3])


def ResNet152():
	return ResNet(Bottleneck, [3, 8, 36, 3])


def test():
	net = ResNet18()
	y = net(torch.randn(1, 3, 32, 32))
	print(y.size())

test()
